2025 Data Science task¶

In [74]:
import pandas as pd
import numpy as np
import seaborn as sns
import plotly.express as px
from matplotlib import pyplot as plt
%matplotlib inline


#html export
import plotly.io as pio
pio.renderers.default = 'notebook'

purple-divider

Dataset creation¶

In [75]:
hiv_df = pd.read_csv(r'HIV data 2000-2023.csv', encoding='ISO-8859-1')
hiv_df.head()
Out[75]:
IndicatorCode Indicator ValueType ParentLocationCode ParentLocation Location type SpatialDimValueCode Location Period type Period Value
0 HIV_0000000001 Estimated number of people (all ages) living w... numeric AFR Africa Country AGO Angola Year 2023 320 000 [280 000 - 380 000]
1 HIV_0000000001 Estimated number of people (all ages) living w... numeric AFR Africa Country AGO Angola Year 2022 320 000 [280 000 - 380 000]
2 HIV_0000000001 Estimated number of people (all ages) living w... numeric AFR Africa Country AGO Angola Year 2021 320 000 [280 000 - 380 000]
3 HIV_0000000001 Estimated number of people (all ages) living w... numeric AFR Africa Country AGO Angola Year 2020 320 000 [280 000 - 370 000]
4 HIV_0000000001 Estimated number of people (all ages) living w... numeric AFR Africa Country AGO Angola Year 2015 300 000 [260 000 - 350 000]

Attributes¶

  • IndicatorCode: A unique identifier for the indicator being measured (e.g., "HIV_0000000001" for the estimated number of people living with HIV).

  • Indicator: A description of the indicator being measured (e.g., "Estimated number of people (all ages) living with HIV").

  • ValueType: Specifies the type of data recorded (e.g., "numeric" for numerical values).

  • ParentLocationCode: A code representing the broader geographical region to which the location belongs (e.g., "AFR" for Africa).

  • ParentLocation: The name of the broader geographical region (e.g., "Africa").

  • Location type: Describes the type of location (e.g., "Country").

  • SpatialDimValueCode: A unique code for the specific location (e.g., "AGO" for Angola).

  • Location: The name of the specific location (e.g., "Angola").

  • Period type: Specifies the type of time period (e.g., "Year").

  • Period: The year for which the data is recorded (e.g., "2023").

  • Value: The estimated number of people living with HIV, often including a range (e.g., "320 000 [280 000 - 380 000]" for Angola in 2023).

This dataset provides detailed information on HIV prevalence across various countries and regions, with data spanning multiple years (2000–2023). The Value field is particularly important as it contains the estimated figures, often accompanied by confidence intervals. The dataset is structured to allow analysis by region, country, and year.

Since the dataset focuses on an estimated number of people living with HIV, the columns IndicatorCode, Indicator, ValueType and Period type are redundant and do not add analytical value to trend analysis hence we drop them

In [76]:
# deleting redundant columns that do not add analytic value 
del hiv_df['IndicatorCode']
del hiv_df['Indicator']
del hiv_df['ValueType']
del hiv_df['Period type']
In [77]:
hiv_df
Out[77]:
ParentLocationCode ParentLocation Location type SpatialDimValueCode Location Period Value
0 AFR Africa Country AGO Angola 2023 320 000 [280 000 - 380 000]
1 AFR Africa Country AGO Angola 2022 320 000 [280 000 - 380 000]
2 AFR Africa Country AGO Angola 2021 320 000 [280 000 - 380 000]
3 AFR Africa Country AGO Angola 2020 320 000 [280 000 - 370 000]
4 AFR Africa Country AGO Angola 2015 300 000 [260 000 - 350 000]
... ... ... ... ... ... ... ...
1547 WPR Western Pacific Country WSM Samoa 2020 No data
1548 WPR Western Pacific Country WSM Samoa 2015 No data
1549 WPR Western Pacific Country WSM Samoa 2010 No data
1550 WPR Western Pacific Country WSM Samoa 2005 No data
1551 WPR Western Pacific Country WSM Samoa 2000 No data

1552 rows × 7 columns

In [78]:
hiv_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1552 entries, 0 to 1551
Data columns (total 7 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   ParentLocationCode   1552 non-null   object
 1   ParentLocation       1552 non-null   object
 2   Location type        1552 non-null   object
 3   SpatialDimValueCode  1552 non-null   object
 4   Location             1552 non-null   object
 5   Period               1552 non-null   int64 
 6   Value                1552 non-null   object
dtypes: int64(1), object(6)
memory usage: 85.0+ KB

Cleaning¶

In [79]:
# duplicates
hiv_df.duplicated().sum()
Out[79]:
0
In [80]:
# nulls
hiv_df.isna().sum()
Out[80]:
ParentLocationCode     0
ParentLocation         0
Location type          0
SpatialDimValueCode    0
Location               0
Period                 0
Value                  0
dtype: int64

There are no duplicates nor null values. However, some entries of the Value field have "No data" or placeholder values like "<200" for very small estimates so we need to clean it to have single values

In [81]:
# entries with the value "No data"
no_data = hiv_df[hiv_df['Value'] == 'No data']
no_data
Out[81]:
ParentLocationCode ParentLocation Location type SpatialDimValueCode Location Period Value
40 AFR Africa Country CAF Central African Republic 2023 No data
41 AFR Africa Country CAF Central African Republic 2022 No data
42 AFR Africa Country CAF Central African Republic 2021 No data
43 AFR Africa Country CAF Central African Republic 2020 No data
44 AFR Africa Country CAF Central African Republic 2015 No data
... ... ... ... ... ... ... ...
1547 WPR Western Pacific Country WSM Samoa 2020 No data
1548 WPR Western Pacific Country WSM Samoa 2015 No data
1549 WPR Western Pacific Country WSM Samoa 2010 No data
1550 WPR Western Pacific Country WSM Samoa 2005 No data
1551 WPR Western Pacific Country WSM Samoa 2000 No data

394 rows × 7 columns

In [82]:
# countries with No data on people living with HIV
no_data['Location'].unique()
Out[82]:
array(['Central African Republic', 'Cameroon', 'Equatorial Guinea',
       'Sao Tome and Principe', 'Seychelles', 'Antigua and Barbuda',
       'Canada', 'Dominica', 'Grenada', 'Saint Kitts and Nevis',
       'Saint Lucia', 'Trinidad and Tobago', 'United States of America',
       'Saint Vincent and the Grenadines', 'Bahrain', 'Andorra',
       'Austria', 'Belgium', 'Cyprus', 'Germany', 'Finland',
       'United Kingdom of Great Britain and Northern Ireland', 'Hungary',
       'Monaco', 'Netherlands (Kingdom of the)', 'Norway', 'Poland',
       'Russian Federation', 'San Marino', 'Sweden', 'Turkmenistan',
       'T\x9frkiye', 'Ukraine', 'Uzbekistan', 'India', 'Maldives',
       "Democratic People's Republic of Korea", 'Brunei Darussalam',
       'China', 'Cook Islands', 'Micronesia (Federated States of)',
       'Japan', 'Kiribati', 'Republic of Korea', 'Marshall Islands',
       'Niue', 'Nauru', 'Palau', 'Solomon Islands', 'Tonga', 'Tuvalu',
       'Vanuatu', 'Samoa'], dtype=object)
In [83]:
# cleaning our dataset entries with the value "No data"
hiv_df = hiv_df[hiv_df['Value'] != 'No data']
hiv_df
Out[83]:
ParentLocationCode ParentLocation Location type SpatialDimValueCode Location Period Value
0 AFR Africa Country AGO Angola 2023 320 000 [280 000 - 380 000]
1 AFR Africa Country AGO Angola 2022 320 000 [280 000 - 380 000]
2 AFR Africa Country AGO Angola 2021 320 000 [280 000 - 380 000]
3 AFR Africa Country AGO Angola 2020 320 000 [280 000 - 370 000]
4 AFR Africa Country AGO Angola 2015 300 000 [260 000 - 350 000]
... ... ... ... ... ... ... ...
1531 WPR Western Pacific Country VNM Viet Nam 2020 250 000 [230 000 - 270 000]
1532 WPR Western Pacific Country VNM Viet Nam 2015 240 000 [210 000 - 260 000]
1533 WPR Western Pacific Country VNM Viet Nam 2010 210 000 [190 000 - 230 000]
1534 WPR Western Pacific Country VNM Viet Nam 2005 180 000 [150 000 - 200 000]
1535 WPR Western Pacific Country VNM Viet Nam 2000 120 000 [100 000 - 140 000]

1158 rows × 7 columns

We have dropped 394 columns with 'No Data' as their value for people living with HIV

In [84]:
# extract the central estimate (320000) as a number for values with the format: 320 000 [280 000 - 380 000]

def extract_value(val):
    if isinstance(val, str):
        # Handle values like "<500" at the beginning
        if val.startswith('<'):
            number = int(val[1:].split()[0])  # take just the number part before any space
            return number - 1  # assume just under that number
        else:
            # Extract the number at the start if it's not a "<" value
            match = pd.Series(val).str.extract(r'^([\d\s]+)').iloc[0, 0]
            if match:
                return float(match.replace(' ', ''))  # remove spaces, convert to float
    return None  # fallback if no match

# Apply to value column and convert to int
hiv_df.loc[:, 'Value'] = hiv_df['Value'].apply(extract_value)
hiv_df['Value'] = hiv_df['Value'].astype(int)


hiv_df

C:\Users\deninjo\AppData\Local\Temp\ipykernel_6524\1950752774.py:18: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[84]:
ParentLocationCode ParentLocation Location type SpatialDimValueCode Location Period Value
0 AFR Africa Country AGO Angola 2023 320000
1 AFR Africa Country AGO Angola 2022 320000
2 AFR Africa Country AGO Angola 2021 320000
3 AFR Africa Country AGO Angola 2020 320000
4 AFR Africa Country AGO Angola 2015 300000
... ... ... ... ... ... ... ...
1531 WPR Western Pacific Country VNM Viet Nam 2020 250000
1532 WPR Western Pacific Country VNM Viet Nam 2015 240000
1533 WPR Western Pacific Country VNM Viet Nam 2010 210000
1534 WPR Western Pacific Country VNM Viet Nam 2005 180000
1535 WPR Western Pacific Country VNM Viet Nam 2000 120000

1158 rows × 7 columns

In [85]:
hiv_df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1158 entries, 0 to 1535
Data columns (total 7 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   ParentLocationCode   1158 non-null   object
 1   ParentLocation       1158 non-null   object
 2   Location type        1158 non-null   object
 3   SpatialDimValueCode  1158 non-null   object
 4   Location             1158 non-null   object
 5   Period               1158 non-null   int64 
 6   Value                1158 non-null   int32 
dtypes: int32(1), int64(1), object(5)
memory usage: 67.9+ KB

purple-divider

Question One¶

Create a visualization that shows the trend of HIV cases in the countries that contribute to 75% of the global burden¶

We'll first group the cleaned dataset by Location and Period, summing up Value.

In [86]:
# 1. Group by year and country to get total HIV cases per country per year
country_year_df = hiv_df.groupby(['Location', 'Period'])['Value'].sum().reset_index()

country_year_df
Out[86]:
Location Period Value
0 Afghanistan 2000 1600
1 Afghanistan 2005 2800
2 Afghanistan 2010 4100
3 Afghanistan 2015 6500
4 Afghanistan 2020 10000
... ... ... ...
1153 Zimbabwe 2015 1400000
1154 Zimbabwe 2020 1300000
1155 Zimbabwe 2021 1300000
1156 Zimbabwe 2022 1300000
1157 Zimbabwe 2023 1300000

1158 rows × 3 columns

We then calculate Total Global HIV Cases Per Year. This helps us determine each country’s contribution to the global burden for each year.

In [87]:
# 2. Total global cases per year
global_yearly_total = country_year_df.groupby('Period')['Value'].sum().reset_index()
global_yearly_total.rename(columns={'Value': 'GlobalTotal'}, inplace=True)

global_yearly_total
Out[87]:
Period GlobalTotal
0 2000 21353916
1 2005 23492604
2 2010 26058607
3 2015 28925842
4 2020 31016146
5 2021 31314227
6 2022 31198307
7 2023 33933987

Merge global total and find country percentage

  • For each year, we already know the total number of HIV cases globally (GlobalTotal).

  • Now, we add that total next to each country’s number for that year.

  • Then, we calculate what percentage each country contributes that year:

Example: If Kenya had 100,000 cases and the world had 1,000,000 cases that year → Kenya’s share = 10%.

In [88]:
# 3. Merge global total back to country-level data
merged_df = country_year_df.merge(global_yearly_total, on='Period')
merged_df['Percent'] = merged_df['Value'] / merged_df['GlobalTotal'] * 100

merged_df
Out[88]:
Location Period Value GlobalTotal Percent
0 Afghanistan 2000 1600 21353916 0.007493
1 Afghanistan 2005 2800 23492604 0.011919
2 Afghanistan 2010 4100 26058607 0.015734
3 Afghanistan 2015 6500 28925842 0.022471
4 Afghanistan 2020 10000 31016146 0.032241
... ... ... ... ... ...
1153 Zimbabwe 2015 1400000 28925842 4.839963
1154 Zimbabwe 2020 1300000 31016146 4.191365
1155 Zimbabwe 2021 1300000 31314227 4.151468
1156 Zimbabwe 2022 1300000 31198307 4.166893
1157 Zimbabwe 2023 1300000 33933987 3.830967

1158 rows × 5 columns

Instead of picking the biggest contributors only based on today (latest year) or on average across years, we are now looking at the total number of cases a country has contributed from 2000 to 2023.

This way, it tells us which countries have been the biggest contributors overall — not just recently, and favors countries that had high numbers consistently across many years, not just a sudden rise or fall.

In [89]:
# 4. Sum total cases per country across all years
country_total = merged_df.groupby('Location')['Value'].sum().reset_index()
global_total_sum = global_yearly_total['GlobalTotal'].sum()

country_total
Out[89]:
Location Value
0 Afghanistan 61000
1 Albania 8208
2 Algeria 126100
3 Angola 2160000
4 Argentina 859000
... ... ...
142 Venezuela (Bolivarian Republic of) 724000
143 Viet Nam 1750000
144 Yemen 78100
145 Zambia 8890000
146 Zimbabwe 10900000

147 rows × 2 columns

We then find the percentage each country contributed to this global total

In [90]:
# 5. Calculate percent contribution of each country
country_total['Percent'] = country_total['Value'] / global_total_sum * 100
country_total
Out[90]:
Location Value Percent
0 Afghanistan 61000 0.026838
1 Albania 8208 0.003611
2 Algeria 126100 0.055479
3 Angola 2160000 0.950313
4 Argentina 859000 0.377925
... ... ... ...
142 Venezuela (Bolivarian Republic of) 724000 0.318531
143 Viet Nam 1750000 0.769929
144 Yemen 78100 0.034361
145 Zambia 8890000 3.911240
146 Zimbabwe 10900000 4.795559

147 rows × 3 columns

Filter Top Contributors Covering 75% of Global Burden by sorting countries by their contribution and cumulatively sum their % share until reaching 75%.

In [91]:
# 6. Sort and compute cumulative percent
country_total = country_total.sort_values('Percent', ascending=False)
country_total['CumulativePercent'] = country_total['Percent'].cumsum()

# 7. Select countries contributing to 75% of total global cases
top_countries = country_total[country_total['CumulativePercent'] <= 75]['Location'].tolist()

top_countries
Out[91]:
['South Africa',
 'Mozambique',
 'Nigeria',
 'Kenya',
 'United Republic of Tanzania',
 'Zimbabwe',
 'Uganda',
 'Zambia',
 'Malawi',
 'Brazil',
 'Thailand',
 'Ethiopia',
 'Democratic Republic of the Congo',
 "Cote d'Ivoire"]
In [92]:
# 8. Filter original country-year data for those top countries
top_country_trend = country_year_df[country_year_df['Location'].isin(top_countries)]

# 9. Plot the trend
fig = px.line(top_country_trend, 
              x='Period', 
              y='Value', 
              color='Location',
              title='Trend of HIV Cases in Top Contributing Countries (75% of Cumulative Global Cases)',
              labels={'Value': 'Estimated HIV Cases', 'Period': 'Year'})

fig.show()

Generate a visualization that displays the trend of HIV cases in the countries contributing to 75% of the burden within each WHO region (column called ParentLocationCode contains the WHO regions)¶

Instead of asking "Who are the biggest players globally?" we are now trying to find out, "Who are the biggest players inside each WHO region?".

First and foremost, for each WHO region, country, and year, we sum the HIV cases.

In [93]:
# 1. Group by year, country, and WHO region to get total HIV cases per country per year
country_year_df = hiv_df.groupby(['ParentLocationCode', 'Location', 'Period'])['Value'].sum().reset_index()

country_year_df
Out[93]:
ParentLocationCode Location Period Value
0 AFR Algeria 2000 2400
1 AFR Algeria 2005 5700
2 AFR Algeria 2010 10000
3 AFR Algeria 2015 15000
4 AFR Algeria 2020 21000
... ... ... ... ...
1153 WPR Viet Nam 2015 240000
1154 WPR Viet Nam 2020 250000
1155 WPR Viet Nam 2021 250000
1156 WPR Viet Nam 2022 250000
1157 WPR Viet Nam 2023 250000

1158 rows × 4 columns

Now, for each WHO region and year, we find the total HIV cases (summing all countries in the region) so that we can find out each country's share within its region later on

In [94]:
# 2. Total regional cases per year
regional_yearly_total = country_year_df.groupby(['ParentLocationCode', 'Period'])['Value'].sum().reset_index()
regional_yearly_total.rename(columns={'Value': 'RegionalTotal'}, inplace=True)

regional_yearly_total
Out[94]:
ParentLocationCode Period RegionalTotal
0 AFR 2000 18088999
1 AFR 2005 19435099
2 AFR 2010 21219799
3 AFR 2015 23436199
4 AFR 2020 24838099
5 AFR 2021 25025199
6 AFR 2022 25102299
7 AFR 2023 25199599
8 AMR 2000 1152900
9 AMR 2005 1459800
10 AMR 2010 1773900
11 AMR 2015 2091600
12 AMR 2020 2455300
13 AMR 2021 2480700
14 AMR 2022 2558000
15 AMR 2023 2631200
16 EMR 2000 98893
17 EMR 2005 158135
18 EMR 2010 243666
19 EMR 2015 313167
20 EMR 2020 422730
21 EMR 2021 452150
22 EMR 2022 486170
23 EMR 2023 529280
24 EUR 2000 577537
25 EUR 2005 746342
26 EUR 2010 937863
27 EUR 2015 1057376
28 EUR 2020 1165097
29 EUR 2021 1184638
30 EUR 2022 864688
31 EUR 2023 849238
32 SEAR 2000 1147589
33 SEAR 2005 1312330
34 SEAR 2010 1427320
35 SEAR 2015 1478780
36 SEAR 2020 1481300
37 SEAR 2021 1491400
38 SEAR 2022 1482600
39 SEAR 2023 3982700
40 WPR 2000 287998
41 WPR 2005 380898
42 WPR 2010 456059
43 WPR 2015 548720
44 WPR 2020 653620
45 WPR 2021 680140
46 WPR 2022 704550
47 WPR 2023 741970

We then find the percentage each country contributed, each year inside its region.

In [95]:
# 3. Merge regional total back to country-level data
merged_df = country_year_df.merge(regional_yearly_total, on=['ParentLocationCode', 'Period'])
merged_df['Percent'] = merged_df['Value'] / merged_df['RegionalTotal'] * 100

merged_df
Out[95]:
ParentLocationCode Location Period Value RegionalTotal Percent
0 AFR Algeria 2000 2400 18088999 0.013268
1 AFR Algeria 2005 5700 19435099 0.029328
2 AFR Algeria 2010 10000 21219799 0.047126
3 AFR Algeria 2015 15000 23436199 0.064004
4 AFR Algeria 2020 21000 24838099 0.084548
... ... ... ... ... ... ...
1153 WPR Viet Nam 2015 240000 548720 43.738154
1154 WPR Viet Nam 2020 250000 653620 38.248524
1155 WPR Viet Nam 2021 250000 680140 36.757138
1156 WPR Viet Nam 2022 250000 704550 35.483642
1157 WPR Viet Nam 2023 250000 741970 33.694085

1158 rows × 6 columns

For each country and region, sum all HIV cases across all years (2000–2023). This tells US how big a contributor the country is overall, not just in a year.

In [96]:
# 4. Sum Cases Across All Years
total_contribution = merged_df.groupby(['ParentLocationCode', 'Location'])['Value'].sum().reset_index()

total_contribution
Out[96]:
ParentLocationCode Location Value
0 AFR Algeria 126100
1 AFR Angola 2160000
2 AFR Benin 542000
3 AFR Botswana 2680000
4 AFR Burkina Faso 901000
... ... ... ...
142 WPR New Zealand 22600
143 WPR Papua New Guinea 392000
144 WPR Philippines 686400
145 WPR Singapore 45000
146 WPR Viet Nam 1750000

147 rows × 3 columns

We then calculate the Total Sum per Region

In [97]:
# 5. Get total sum per region for percentage calculation
regional_total_sum = total_contribution.groupby('ParentLocationCode')['Value'].sum().reset_index()
regional_total_sum.rename(columns={'Value': 'RegionalTotalSum'}, inplace=True)


regional_total_sum
Out[97]:
ParentLocationCode RegionalTotalSum
0 AFR 182345292
1 AMR 16603400
2 EMR 2704191
3 EUR 7382779
4 SEAR 13804019
5 WPR 4453955

We merge the total region sums into country-level sums then calculate what % each country contributed overall to its WHO region (across all years).

In [98]:
# 6. Merge to calculate each country's % contribution within its region
total_contribution = total_contribution.merge(regional_total_sum, on='ParentLocationCode')
total_contribution['Percent'] = total_contribution['Value'] / total_contribution['RegionalTotalSum'] * 100

total_contribution
Out[98]:
ParentLocationCode Location Value RegionalTotalSum Percent
0 AFR Algeria 126100 182345292 0.069155
1 AFR Angola 2160000 182345292 1.184566
2 AFR Benin 542000 182345292 0.297238
3 AFR Botswana 2680000 182345292 1.469739
4 AFR Burkina Faso 901000 182345292 0.494118
... ... ... ... ... ...
142 WPR New Zealand 22600 4453955 0.507414
143 WPR Papua New Guinea 392000 4453955 8.801167
144 WPR Philippines 686400 4453955 15.411022
145 WPR Singapore 45000 4453955 1.010338
146 WPR Viet Nam 1750000 4453955 39.290922

147 rows × 5 columns

Sort countries inside each WHO region from highest contributor to lowest, then cumulatively add up percentages within each region.

In [99]:
# 7. Sort and get cumulative percentage
total_contribution = total_contribution.sort_values(['ParentLocationCode', 'Percent'], ascending=[True, False])
total_contribution['CumulativePercent'] = total_contribution.groupby('ParentLocationCode')['Percent'].cumsum()

total_contribution
Out[99]:
ParentLocationCode Location Value RegionalTotalSum Percent CumulativePercent
35 AFR South Africa 51400000 182345292 28.188279 28.188279
28 AFR Mozambique 15150000 182345292 8.308413 36.496692
31 AFR Nigeria 15000000 182345292 8.226152 44.722844
20 AFR Kenya 12000000 182345292 6.580921 51.303765
39 AFR United Republic of Tanzania 11600000 182345292 6.361557 57.665322
... ... ... ... ... ... ...
139 WPR Lao People's Democratic Republic 104400 4453955 2.343984 98.133008
145 WPR Singapore 45000 4453955 1.010338 99.143346
142 WPR New Zealand 22600 4453955 0.507414 99.650760
138 WPR Fiji 11358 4453955 0.255009 99.905769
141 WPR Mongolia 4197 4453955 0.094231 100.000000

147 rows × 6 columns

Keep only the top countries whose cumulative contribution is <= 75% in each WHO region.

In [100]:
# 8. Select countries contributing to 75% within each region
top_countries_per_region = (
    total_contribution[total_contribution['CumulativePercent'] <= 75]
)

top_countries_per_region
Out[100]:
ParentLocationCode Location Value RegionalTotalSum Percent CumulativePercent
35 AFR South Africa 51400000 182345292 28.188279 28.188279
28 AFR Mozambique 15150000 182345292 8.308413 36.496692
31 AFR Nigeria 15000000 182345292 8.226152 44.722844
20 AFR Kenya 12000000 182345292 6.580921 51.303765
39 AFR United Republic of Tanzania 11600000 182345292 6.361557 57.665322
41 AFR Zimbabwe 10900000 182345292 5.977670 63.642992
38 AFR Uganda 10500000 182345292 5.758306 69.401298
40 AFR Zambia 8890000 182345292 4.875366 74.276664
47 AMR Brazil 6070000 16603400 36.558777 36.558777
61 AMR Mexico 2190000 16603400 13.190070 49.748847
50 AMR Colombia 1430000 16603400 8.612694 58.361540
58 AMR Haiti 1010000 16603400 6.083091 64.444632
42 AMR Argentina 859000 16603400 5.173639 69.618271
68 AMR Venezuela (Bolivarian Republic of) 724000 16603400 4.360553 73.978824
80 EMR Pakistan 1247600 2704191 46.135794 46.135794
72 EMR Iran (Islamic Republic of) 327000 2704191 12.092341 58.228136
84 EMR Sudan 273000 2704191 10.095441 68.323576
71 EMR Egypt 154400 2704191 5.709656 74.033232
125 EUR Ukraine 1430000 7382779 19.369400 19.369400
99 EUR France 1285000 7382779 17.405370 36.774770
122 EUR Spain 1090000 7382779 14.764088 51.538858
106 EUR Italy 991000 7382779 13.423130 64.961988
116 EUR Portugal 344000 7382779 4.659492 69.621480
101 EUR Germany 323000 7382779 4.375046 73.996526
134 SEAR Thailand 5430000 13804019 39.336370 39.336370
130 SEAR Indonesia 3513000 13804019 25.449110 64.785480
146 WPR Viet Nam 1750000 4453955 39.290922 39.290922
144 WPR Philippines 686400 4453955 15.411022 54.701945
137 WPR Cambodia 639000 4453955 14.346800 69.048744
In [101]:
# 9. Merge ParentLocation (full name) into top_countries_per_region

# get ParentLocation mappings from hiv_df
region_names = hiv_df[['ParentLocationCode', 'ParentLocation']].drop_duplicates()

# Merge region names into your top_countries_per_region
top_countries_per_region = top_countries_per_region.merge(region_names, on='ParentLocationCode', how='left')


# 10. Now filter your original merged_df for only the top countries
top_country_trend = merged_df[
    merged_df['Location'].isin(top_countries_per_region['Location'])
]

# Also merge to get ParentLocation for plotting
top_country_trend = top_country_trend.merge(region_names, on='ParentLocationCode', how='left')

# 9. Plot the trend
fig = px.line(
    top_country_trend,
    x='Period',
    y='Value',
    color='Location',
    facet_col='ParentLocation',  # <--- This will separate by WHO region nicely
    facet_col_wrap=2,  # Wrap facets into multiple rows if too many regions
    title='Trend of HIV Cases in Top Contributing Countries (75% Global Burden per Region)',
    labels={
        'Value': 'Estimated HIV Cases',
        'Period': 'Year',
        'Location': 'Country'
    }
)

fig.update_layout(height=800)
fig.show()